Applications of deep learning in fundus images: A review
The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …
importance. Due to its powerful performance, deep learning is becoming more and more …
Retinal vessel segmentation, a review of classic and deep methods
A Khandouzi, A Ariafar, Z Mashayekhpour… - Annals of Biomedical …, 2022 - Springer
Retinal illnesses such as diabetic retinopathy (DR) are the main causes of vision loss. In the
early recognition of eye diseases, the segmentation of blood vessels in retina images plays …
early recognition of eye diseases, the segmentation of blood vessels in retina images plays …
A comprehensive survey on segmentation techniques for retinal vessel segmentation
In recent years, enormous research has been carried out on the segmentation of blood
vessels. Segmentation of blood vessels in retinal images is crucial for diagnosing, treating …
vessels. Segmentation of blood vessels in retinal images is crucial for diagnosing, treating …
Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image
The accurate segmentation of cerebral vessels from time-of-flight magnetic resonance
angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular …
angiography (TOF-MRA) data is crucial for the diagnosis and treatment of cerebrovascular …
Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images
The real-time sharing and retrieval of medical data, such as medical imaging data, via cloud
systems can facilitate timely medical/disease diagnosis, for example during pandemics (eg …
systems can facilitate timely medical/disease diagnosis, for example during pandemics (eg …
SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
GX Xu, CX Ren - Neurocomputing, 2023 - Elsevier
Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently,
convolutional neural networks have shown significant ability to extract the blood vessel …
convolutional neural networks have shown significant ability to extract the blood vessel …
Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework
X Wang, X Jiang - Signal processing, 2019 - Elsevier
Accurate vessel segmentation is a fundamental and challenging task for the retinal fundus
image analysis. Current approaches typically train a global discriminative model for retinal …
image analysis. Current approaches typically train a global discriminative model for retinal …
TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation
N Lv, L Xu, Y Chen, W Sun, J Tian, S Zhang - Scientific Reports, 2024 - nature.com
Accurate segmentation of retinal blood vessels is crucial for enhancing diagnostic efficiency
and preventing disease progression. However, the small size and complex structure of …
and preventing disease progression. However, the small size and complex structure of …
Segmentation of ultrasound brachial plexus based on U-Net
Y Wang, J Geng, C Zhou… - … international conference on …, 2021 - ieeexplore.ieee.org
Brachial plexus block anesthesia (PNB) is one of the anesthesia methods commonly used
by anesthesiologists in surgical operations. Anesthesiologists use ultrasonic equipment to …
by anesthesiologists in surgical operations. Anesthesiologists use ultrasonic equipment to …
Minimizing-Entropy and Fourier Consistency Network for Domain Adaptation on Optic Disc and Cup Segmentation
SP Xu, TB Li, ZQ Zhang, D Song - IEEE Access, 2021 - ieeexplore.ieee.org
Automated segmentation of the optic disc (OD) and optic cup (OC) from different datasets
plays an important role in the diagnosis of glaucoma and greatly saves human resources in …
plays an important role in the diagnosis of glaucoma and greatly saves human resources in …